Fuel Cycle System Engineering Technology Development Division, Korea Atomic Energy Research Institute, Daejeon, 305-353, Korea.
Sensors (Basel). 2011;11(9):8751-68. doi: 10.3390/s110908751. Epub 2011 Sep 9.
In this paper, we propose a simultaneous intrinsic and extrinsic parameter identification of a hand-mounted laser-vision sensor (HMLVS). A laser-vision sensor (LVS), consisting of a camera and a laser stripe projector, is used as a sensor component of the robotic measurement system, and it measures the range data with respect to the robot base frame using the robot forward kinematics and the optical triangulation principle. For the optimal estimation of the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. Best-fit parameters, including both the intrinsic and extrinsic parameters of the HMLVS, are simultaneously obtained based on the least-squares criterion. From the simulation and experimental results, it is shown that the parameter identification problem considered was characterized by a highly multimodal landscape; thus, the global optimization technique such as a particle swarm optimization can be a promising tool to identify the model parameters for a HMLVS, while the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum. The proposed optimization method does not require good initial guesses of the system parameters to converge at a very stable solution and it could be applied to a kinematically dissimilar robot system without loss of generality.
在本文中，我们提出了一种对手持激光视觉传感器（HMLVS）的同时内在和外在参数识别方法。激光视觉传感器（LVS）由相机和激光条纹投影仪组成，作为机器人测量系统的传感器组件，它使用机器人运动学和光学三角测量原理来测量相对于机器人基坐标系的距离数据。为了对模型参数进行最优估计，我们应用了两种优化技术：非线性最小二乘优化器和粒子群优化器。根据最小二乘准则，同时获得了包括 HMLVS 的内在和外在参数在内的最佳拟合参数。从仿真和实验结果可以看出，所考虑的参数识别问题的特点是具有高度多模态景观；因此，全局优化技术（如粒子群优化）可以成为识别 HMLVS 模型参数的有前途的工具，而非线性最小二乘优化器即使在初始候选解接近真实最优值时，也常常无法找到最优解。所提出的优化方法不需要系统参数的良好初始猜测即可收敛到非常稳定的解，并且可以应用于运动学上不同的机器人系统，而不会失去一般性。
